> Markdown version of [/jobs/ext/4613387-staff-site-reliability-engineer](https://www.wearedevelopers.com/jobs/ext/4613387-staff-site-reliability-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Site Reliability Engineer - **Company:** Stratitech Services LLC - **Location:** San Francisco, CA, United States - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Bash Shell, Continuous Integration, Software Debugging, Linux, DevOps, Distributed Systems, Github, Python (Programming Language), Machine Learning, Performance Tuning, Reliability Engineering, Prometheus, Azure Machine Learning, Datadog, Scripting, Grafana, Apache Spark, Kubernetes, Deployment Automation, Apache Kafka, Data Management, Machine Learning Operations, Docker - **Published:** November 7, 2026 - **Apply:** https://www.juju.com/job/28_25197_a5949f0ff6e5679d08eea3b7306d2f00 ## About the Role * Deep experience operating Linux infrastructure and networking in production environments * Proven impact as a Staff SRE, Senior SRE, or senior-level DevOps/Platform Engineer supporting distributed systems * Experience supporting complex, data-intensive or ML-driven systems in production * Strong hands-on experience with Docker and Kubernetes * Strong scripting ability (Bash and/or Python) * CI/CD ownership experience (GitHub Actions, ArgoCD, or similar) * Experience with modern observability stacks (Prometheus, Grafana, Datadog, ELK, OpenTelemetry) * Ability to debug systemic failures across infrastructure, deployments, and workloads * Clear communicator who works effectively across engineering and data teams * Engineers who have evolved from infrastructure foundations into strategic reliability leaders will thrive here., * Experience operating ML platforms at scale (training + inference) * AWS or cloud-managed services experience * Exposure to data platforms such as Spark, Airflow, or Kafka * Experience in SOC 2 or regulated environments ## Description You will define how mission-critical machine learning and real-time analytics systems operate in production - influencing reliability strategy, deployment standards, and infrastructure architecture across engineering. This team operates in a highly collaborative, in-person engineering environment in SOMA. Infrastructure, ML, and engineering leaders work side by side to design, build, and operate complex systems in real time. The pace is fast, the feedback loops are tight, and decisions happen quickly. If you've grown from Linux systems * DevOps * Staff-level SRE , and you now think in terms of systemic risk, scalability, and long-term reliability strategy - this role gives you direct influence and visibility. This role is intentionally in-person because: * Reliability decisions happen at architectural depth - not over Slack threads * ML, data, and infrastructure teams collaborate continuously in real time * Post-incident reviews, system design debates, and performance tuning sessions are hands-on and high impact You will have direct access to engineering leadership and decision-makers The infrastructure you're operating is mission-critical and evolving quickly If you value deep technical collaboration, tight feedback loops, and being at the center of high-scale ML systems - this environment is built for that. What You'll Own * Production reliability for ML and real-time analytics workloads * CI/CD strategy, deployment automation, and rollback design * Observability frameworks (SLOs, alerting, monitoring, incident response) * Infrastructure-as-Code and Kubernetes environments * Capacity planning and performance optimization * Post-incident reviews that drive measurable, long-term reliability improvements * Reliability standards across teams - not just within a single service * You'll partner directly with engineering and data science teams to ensure ML workloads are production-ready and reliable by design.